我正在尝试使用LSTM自动编码器将可变大小的整数序列的数据集嵌入固定长度的向量中，但是即使序列不同，该模型也会继续输出恒定向量。
数据集的每个样本表示如下：
每个序列都使用一键编码进行编码：
在序列较短的情况下，将零填充应用于单编码的矢量。
最后，我输入了一个大小为
的矩阵我期望从模型输出的是大小矩阵
from keras.utils import Sequence
def to_categorical(sequences, n_categories, max_len):
categorical_sequences = []
for s in sequences:
#ohe = np.full((max_len, n_categories), fill_value=-1 )
ohe = np.zeros((max_len, n_categories))
for i, item in enumerate(s):
ohe[i][item] = 1
categorical_sequences.append(ohe)
return np.array(categorical_sequences)
class batch_generator(Sequence):
def __init__(self, X, batch_size, num_classes, max_len, y=None, prediction_only=False, shuffle=True):
self.X = X
self.batch_size = batch_size
self.num_classes = num_classes
self.max_len = max_len
self.y = y
self.prediction_only = prediction_only
self.shuffle = shuffle
self.on_epoch_end()
def __len__(self):
'Denotes the number of batches per epoch'
return int(np.floor(len(self.X) / self.batch_size))
def __getitem__(self, index):
'Generate one batch of data'
#print("Generating batch with index {}".format(index))
batch_indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]
return self.__data_generation(batch_indexes)
def on_epoch_end(self):
'Updates indexes after each epoch'
self.indexes = np.arange(len(self.X))
if(self.shuffle == True):
np.random.shuffle(self.indexes, )
def __data_generation(self, batch_indexes):
'Generates data containing batch_size samples'
result = None
batch_X = to_categorical(self.X[batch_indexes], self.num_classes, self.max_len)
if(self.prediction_only):
result = batch_X
else:
if(self.y is None):
result = batch_X, batch_X
else:
batch_y = self.y[batch_indexes]
result = batch_X, batch_y
return result
from keras.layers import Input, RepeatVector, CuDNNGRU
from keras.models import Model
n_timesteps = np.max([x.shape[0] for x in X])
n_features = int(np.max([np.max(x) for x in X]) + 1)
latent_dim = 128
print("N timesteps {}".format(n_timesteps))
print("N features {}".format(n_features))
print("Latent dimension {}".format(latent_dim))
inputs = Input(shape=(n_timesteps, n_features))
encoded = CuDNNGRU(units=latent_dim)(inputs)
decoded = RepeatVector(n=n_timesteps)(encoded)
decoded = CuDNNGRU(units=n_features, return_sequences=True)(decoded)
autoencoder = Model(inputs, decoded)
encoder = Model(inputs, encoded)
autoencoder.compile(loss='mse', optimizer="adam")
autoencoder.summary()
batch_size = 128
train_generator = batch_generator(X_train, batch_size=batch_size, num_classes=n_features, max_len=n_timesteps)
val_generator = batch_generator(X_val, batch_size=batch_size, num_classes=n_features, max_len=n_timesteps)
history = autoencoder.fit_generator(generator = train_generator,
steps_per_epoch = X_train.shape[0]//batch_size,
epochs = 2,
#callbacks = [early_stopping, model_checkpoint],
validation_data = val_generator,
validation_steps = X_val.shape[0]//batch_size,
#use_multiprocessing = True,
#workers = n_cpu
)
X_generator = batch_generator(X, batch_size=batch_size, num_classes=n_features, max_len=n_timesteps, prediction_only=True )
compact_representation64 = encoder.predict_generator(generator=X_generator, steps=X.shape[0]//batch_size, verbose=1)
问题在于每个样本都被编码为相同的固定长度向量：
array（[-0.00898637，0.02220072，-0.0095799，0.00655961，0.00733364， 0.00351852，0.00088661，-0.00060489，-0.00819919，-0.01798768， -0.02408937，-0.01549、0.00395884，-0.0124888，-0.00321282， -0.01447861，............................................
array（[-0.00898637，0.02220072，-0.0095799，0.00655961，0.00733364， 0.00351852，0.00088661，-0.00060489，-0.00819919，-0.01798768， -0.02408937，-0.01549、0.00395884，-0.0124888，-0.00321282， -0.01447861，............................................
答案 0 :(得分：0)
在我看来，您想要的是：
inputs = Input(shape=(n_timesteps, n_features))
encoded = CuDNNGRU(units=latent_dim, return_sequences=True)(inputs)
decoded = CuDNNGRU(units=n_features)(decoded)
通过添加RepeatVector层，您将创建一个常量序列，该序列将被馈送到第二个CuDNNGRU层。因此输出恒定。